
# Suppressing warnings:
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn

#import necessary libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.impute import SimpleImputer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn import svm
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, VotingClassifier, VotingRegressor
from sklearn.metrics import f1_score, mean_squared_error, confusion_matrix, accuracy_score, classification_report
from sklearn import preprocessing, metrics
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go


# Load the dataset
df = pd.read_csv('weatherdata.csv')
# Display the first few rows of the dataframe
print(df.head())

       Date  Temp9am  Temp3pm  MinTemp  MaxTemp  Rainfall RainToday  \
0  1/2/2013     20.7     20.9     19.5     22.4      15.6       Yes   
1  2/2/2013     22.4     24.8     19.5     25.6       6.0       Yes   
2  3/2/2013     23.5     23.0     21.6     24.5       6.6       Yes   
3  4/2/2013     21.4     20.9     20.2     22.8      18.8       Yes   
4  5/2/2013     22.5     25.5     19.7     25.7      77.4       Yes   

   Evaporation  Sunshine WindGustDir  ...  WindDir9am WindDir3pm WindSpeed9am  \
0          6.2       0.0           W  ...           S        SSW         17.0   
1          3.4       2.7           W  ...           W          E          9.0   
2          2.4       0.1           W  ...         ESE        ESE         17.0   
3          2.2       0.0           W  ...         NNE          E         22.0   
4          4.8       0.0           W  ...         NNE          W         11.0   

   WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  \
0          20.0         92.0         84.0       1017.6       1017.4       8.0   
1          13.0         83.0         73.0       1017.9       1016.4       7.0   
2           2.0         88.0         86.0       1016.7       1015.6       7.0   
3          20.0         83.0         90.0       1014.2       1011.8       8.0   
4           6.0         88.0         74.0       1008.3       1004.8       8.0   

   Cloud3pm  
0       8.0  
1       7.0  
2       8.0  
3       8.0  
4       8.0  

[5 rows x 21 columns]


#DATA CLEANING
# Handle Missing Values
# Let's fill missing numerical values with the mean of their respective columns
numerical_cols = df.select_dtypes(include=['float64', 'int64']).columns
df[numerical_cols] = df[numerical_cols].fillna(df[numerical_cols].mean())


# For categorical variables, let's fill missing values with the most frequent value
categorical_cols = df.select_dtypes(include=['object']).columns
df[categorical_cols] = df[categorical_cols].fillna(df[categorical_cols].mode().iloc[0])


# Convert Categorical Variables into Numerical using One-Hot Encoding for categorical variables
one_hot_encoded_df = pd.get_dummies(df, columns=categorical_cols)


# Separate Features and Target Variable
X = df.drop(['Date', 'RainToday', 'Rainfall'], axis=1)  # Features
y = df['Rainfall']  # Target variable


#Data Analysis: feature distribution
#MIN TEMP
sns.displot(df, x="MinTemp", hue='RainToday', kde=True)
plt.title("Minimum Temperature Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#MAX TEMP
sns.displot(df, x="MaxTemp", hue='RainToday', kde=True)
plt.title("Maximum Temperature Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#WINDGUSTDIST
sns.displot(df, x="WindGustSpeed", hue='RainToday', kde=True)
plt.title("Wind Gust Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#WINDGUSTDIST 9AM
sns.displot(df, x="WindSpeed9am", hue='RainToday', kde=True)
plt.title("WindSpeed at 9am Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#WINDGUSTDIST 3PM
sns.displot(df, x="WindSpeed3pm", hue='RainToday', kde=True)
plt.title("WindSpeed at 3pm Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#HUMIDITY 9AM
sns.displot(df, x="Humidity9am", hue='RainToday', kde=True)
plt.title("Humidity at 9am Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#HUMIDITY 3PM
sns.displot(df, x="Humidity3pm", hue='RainToday', kde=True)
plt.title("Humidity at 3pm Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#PRESSURE 9AM
plt.title("Pressure at 9am Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#PRESSURE 3PM
sns.displot(df, x="Pressure3pm", hue='RainToday', kde=True)
plt.title("Pressure at 3pm Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#CLOUD-DIST 9AM
sns.displot(df, x="Cloud9am", hue='RainToday', kde=True)
plt.title("Cloud at 9am Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#CLOUD-DIST 3PM
sns.displot(df, x="Cloud3pm", hue='RainToday', kde=True)
plt.title("Cloud at 3pm Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#CLOUD-DIST 9AM
sns.displot(df, x="Temp9am", hue='RainToday', kde=True)
plt.title("Temperature at 9am Distribution", fontsize = 14)
plt.show()


#Data Analysis: feature distribution
#CLOUD-DIST 3PM
sns.displot(df, x="Temp3pm", hue='RainToday', kde=True)
plt.title("Temperature at 3pm Distribution", fontsize = 14)
plt.show()


#Data Analysis: EDA
#AVG WINDSPEED ANALYSIS
# Convert 'Date' column to string format
df['Date'] = df['Date'].astype(str)
windspeed_weather_df = df.groupby(df['Date'].str[3:5])[['WindSpeed9am', 'WindSpeed3pm']].mean()
windspeed_weather_df = windspeed_weather_df.reset_index()
windspeed_weather_df


#Data Analysis: EDA
#AVG WINDSPEED OBSERVATION (LINE PLOT) 
x = windspeed_weather_df.loc[:, 'Date']
y1 = windspeed_weather_df['WindSpeed9am'] 
y2 = windspeed_weather_df['WindSpeed3pm']
plt.figure(figsize = (15, 8))
plt.plot(x, y1, marker='D', color = 'darkgreen', label = 'WindSpeed at 9am') 
plt.plot(x, y2, marker='D', color = 'darkorange', label = 'WindSpeed at 3pm')
plt.xlabel('Month', fontsize = 14)
plt.ylabel('WindSpeed', fontsize = 14)
plt.title('Month-wise observation of Average WindSpeed', fontsize = 18)
plt.legend(fontsize = 10, loc = 'best')
plt.show()


#Data Analysis: EDA
#AVG HUMIDITY ANALYSIS
humidity_weather_df = df.groupby(df['Date'].str[3:5])[['Humidity9am', 'Humidity3pm']].mean()
humidity_weather_df = humidity_weather_df.reset_index()
humidity_weather_df


#Data Analysis: EDA
#AVG HUMIDITY ANALYSIS OBSERVATION (STACKED BAR PLOT)
x = humidity_weather_df.loc[:, 'Date']
y1 = humidity_weather_df['Humidity9am'] 
y2 = humidity_weather_df['Humidity3pm']
plt.figure(figsize = (15, 8))
plt.bar(x, y1, color = 'gold', label = 'Humidity at 9am') 
plt.bar(x, y2, color = 'coral',label = 'Humidity at 3pm')
plt.xlabel('Month', fontsize = 14)
plt.ylabel('Humidity', fontsize = 14)
plt.title('Month-wise observation of Average Humidity', fontsize = 18)
plt.legend(fontsize = 10, loc = 'best')
plt.show()


#Data Analysis: EDA
#AVG PRESSURE ANALYSIS
pressure_weather_df = df.groupby(df['Date'].str[3:5])[['Pressure9am', 'Pressure3pm']].mean()
pressure_weather_df = pressure_weather_df.reset_index()
pressure_weather_df


#Data Analysis: EDA
#AVG PRESSURE ANALYSIS (LINE PLOT)
x = pressure_weather_df.loc[:, 'Date']
y1 = pressure_weather_df['Pressure9am'] 
y2 = pressure_weather_df['Pressure3pm']
plt.figure(figsize = (15, 8))
plt.plot(x, y1, marker='o', color = 'purple', label = 'Pressure at 9am') 
plt.plot(x, y2, marker='o', color = 'darkcyan', label = 'Pressure at 3pm')
plt.xlabel('Month', fontsize = 14)
plt.ylabel('Pressure', fontsize = 14)
plt.title('Month-wise observation of Average Pressure', fontsize = 18)
plt.legend(fontsize = 10, loc = 'best')
plt.show()


#Data Analysis: EDA
#Correlation and Description of the data
bf=df.drop(columns=['Date','RainToday','WindGustDir', 'WindDir9am', 'WindDir3pm'])
bf.corr()


df.describe()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3272 entries, 0 to 3271
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Date           3272 non-null   object 
 1   Temp9am        3272 non-null   float64
 2   Temp3pm        3272 non-null   float64
 3   MinTemp        3272 non-null   float64
 4   MaxTemp        3272 non-null   float64
 5   Rainfall       3272 non-null   float64
 6   RainToday      3272 non-null   int32  
 7   Evaporation    3272 non-null   float64
 8   Sunshine       3272 non-null   float64
 9   WindGustDir    3272 non-null   int32  
 10  WindGustSpeed  3272 non-null   float64
 11  WindDir9am     3272 non-null   int32  
 12  WindDir3pm     3272 non-null   int32  
 13  WindSpeed9am   3272 non-null   float64
 14  WindSpeed3pm   3272 non-null   float64
 15  Humidity9am    3272 non-null   float64
 16  Humidity3pm    3272 non-null   float64
 17  Pressure9am    3272 non-null   float64
 18  Pressure3pm    3272 non-null   float64
 19  Cloud9am       3272 non-null   float64
 20  Cloud3pm       3272 non-null   float64
dtypes: float64(16), int32(4), object(1)
memory usage: 485.8+ KB


#Data Analysis: EDA
plt.figure(figsize=(20, 20))
sns.heatmap(bf.corr(), annot=True)

<Axes: >


#Data Preprocessing
#Standardize the Variables
scaler = StandardScaler()
scaler.fit(bf)
scaled_features = scaler.transform(bf)
df_feat = pd.DataFrame(scaled_features,columns=bf.columns[:16])
selected_columns = df[['Date','RainToday','WindGustDir', 'WindDir9am', 'WindDir3pm']]
df_feat[['Date','RainToday','WindGustDir', 'WindDir9am', 'WindDir3pm']] = selected_columns.copy()
df_feat.head()


#Transforming Categorical Variables:to convert categorical variables to binary variables to select what only neeed
df_proc = pd.get_dummies(data=df_feat, columns=['WindGustDir', 'WindDir9am', 'WindDir3pm'])
df_proc.head()


# replace the values of the 'RainToday' column changing them from a categorical column to a binary column. 
df_proc.replace(['No', 'Yes'], [0,1], inplace=True)
df_proc[['Date','RainToday']]


df_proc.drop('Date',axis=1,inplace=True)


#Data Preprocessing
#Rainfall Occurance Prediction
df_proc = df_proc.astype(float)
features = df_proc.drop(columns='RainToday', axis=1)
Y = df_proc['RainToday']
x_train, x_test, y_train, y_test = train_test_split(features, Y, test_size=0.35, random_state=101)


#Logistic Regression Model
LR = LogisticRegression()
LR.fit(x_train,y_train)

LogisticRegression()

LogisticRegression()


#KNN Classification Model
KNN = KNeighborsClassifier(n_neighbors=4)
KNN.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=4)

KNeighborsClassifier(n_neighbors=4)


#Decision Tree Classification Model
Tree = DecisionTreeClassifier()
Tree.fit(x_train,y_train)

DecisionTreeClassifier()

DecisionTreeClassifier()


#SVM Classification Model
SVM = svm.SVC(kernel='linear')
SVM.fit(x_train, y_train)

SVC(kernel='linear')

SVC(kernel='linear')


#Random Forest Classification Model
RFC = RandomForestClassifier(n_estimators=100)
RFC.fit(x_train, y_train)

RandomForestClassifier()

RandomForestClassifier()


#Classification Report on Ranfall Occurance Prediction
LR_Accuracy_Score=metrics.accuracy_score(LR.predict(x_test),y_test)
KNN_Accuracy_Score=metrics.accuracy_score(KNN.predict(x_test.values),y_test)
Tree_Accuracy_Score=metrics.accuracy_score(Tree.predict(x_test.values),y_test)
SVM_Accuracy_Score=metrics.accuracy_score(SVM.predict(x_test.values),y_test)
RFC_Accuracy_Score=metrics.accuracy_score(RFC.predict(x_test.values),y_test)
eclf_Accuracy_Score=metrics.accuracy_score(eclf.predict(x_test.values),y_test)

LR_F1_Score=metrics.f1_score(LR.predict(x_test),y_test)
KNN_F1_Score=metrics.f1_score(KNN.predict(x_test.values),y_test,average='weighted')
Tree_F1_Score=metrics.f1_score(Tree.predict(x_test.values),y_test)
SVM_F1_Score=metrics.f1_score(SVM.predict(x_test.values),y_test)
RFC_F1_Score=metrics.f1_score(RFC.predict(x_test.values),y_test)
eclf_F1_Score=metrics.f1_score(eclf.predict(x_test.values),y_test)

Report = {'Classification Algorithm': ['Logistic Regression','KNN', 'Decision Tree',  'SVM','Random Forest','Ensembled Classifier'],
          'Accuracy Score': [LR_Accuracy_Score,KNN_Accuracy_Score, Tree_Accuracy_Score, SVM_Accuracy_Score, RFC_Accuracy_Score,eclf_Accuracy_Score],
          'F1-score': [LR_F1_Score,KNN_F1_Score, Tree_F1_Score, SVM_F1_Score, RFC_F1_Score,eclf_F1_Score],}
Report = pd.DataFrame(Report)
Report


#visualizing of accuracies 
fig = make_subplots(rows=1, cols=1)
fig.add_trace(
    go.Bar(x=Report['Classification Algorithm'], y=Report['Accuracy Score'], name='Accuracy Score'),
    row=1, col=1
)

fig.add_trace(
    go.Bar(x=Report['Classification Algorithm'], y=Report['F1-score'], name='F1-score'), row=1,col=1
)
fig.update_layout(title_text="Accuracy Scores Of Classification Algorithms")
fig.update_yaxes(title_text="Score", row=1, col=1)
fig.update_xaxes(title_text="Classification Algorithm", row=1, col=1)
fig.show()


#Data Preprocessing
#Rainfall Amount Prediction
df_proc = df_proc.astype(float)
features = df_proc.drop(columns='Rainfall', axis=1)
Y = df_proc['Rainfall']
x_train, x_test, y_train, y_test = train_test_split(features, Y, test_size=0.35, random_state=101)


#Linear Regression
LinearReg = LinearRegression()
LinearReg.fit(x_train, y_train)

LinearRegression()

LinearRegression()


#Random Forest Regression Model
RFR = RandomForestRegressor()
RFR.fit(x_train, y_train)

RandomForestRegressor()

RandomForestRegressor()


# SVM Regression Model
SVRR = SVR()
SVRR.fit(x_train, y_train)

SVR()

SVR()


#Regression report on rainfall amount prediction
LinearRegression_MAE = metrics.mean_absolute_error(LinearReg.predict(x_test), y_test)
LinearRegression_MSE = metrics.mean_squared_error(LinearReg.predict(x_test), y_test, squared=False)

RandomForest_MAE = metrics.mean_absolute_error(RFR.predict(x_test), y_test)
RandomForest_MSE = metrics.mean_squared_error(RFR.predict(x_test), y_test, squared=False)

SVM_MAE = metrics.mean_absolute_error(SVRR.predict(x_test), y_test)
SVM_MSE = metrics.mean_squared_error(SVRR.predict(x_test), y_test, squared=False)

Report = {'Regression Algorithm': ['Linear Regression','Random Forest', 'SVM'],
          'MAE': [LinearRegression_MAE,RandomForest_MAE, SVM_MAE],
          'RMSE': [LinearRegression_MSE,RandomForest_MSE, SVM_MSE],}
Report = pd.DataFrame(Report)
Report


#visualizing of accuracies 
fig = make_subplots(rows=1, cols=1)

fig.add_trace(
    go.Bar(x=Report['Regression Algorithm'], y=Report['MAE'], name='MAE'),
    row=1, col=1
)

fig.add_trace(
    go.Bar(x=Report['Regression Algorithm'], y=Report['RMSE'], name='RMSE'), row=1,col=1
)
fig.update_layout(title_text="Error of Regression Algorithms")
fig.update_yaxes(title_text="Error", row=1, col=1)
fig.update_xaxes(title_text="Regression Algorithm", row=1, col=1)
fig.show()


#Average Temperature of a day Prediction
df_proc = df_proc.astype(float)
df_proc['AvgTemp']=df_proc[['Temp9am', 'Temp3pm','MinTemp','MaxTemp']].mean(axis=1)
features = df_proc.drop(columns=['Temp9am', 'Temp3pm','MinTemp','MaxTemp','AvgTemp'], axis=1)
Y = df_proc['AvgTemp']
x_train, x_test, y_train, y_test = train_test_split(features, Y, test_size=0.35, random_state=101)


#Linear Regression
LinearReg = LinearRegression()
LinearReg.fit(x_train, y_train)

LinearRegression()

LinearRegression()


#Random Forest Regression Model
RFR = RandomForestRegressor()
RFR.fit(x_train, y_train)

RandomForestRegressor()

RandomForestRegressor()


#SVM Regression Model
SVR=SVR()
SVR.fit(x_train, y_train)

SVR()

SVR()


#Regression report on average temperature prediction
LinearRegression_MAE = metrics.mean_absolute_error(LinearReg.predict(x_test), y_test)
LinearRegression_MSE = metrics.mean_squared_error(LinearReg.predict(x_test), y_test, squared=False)

RandomForest_MAE = metrics.mean_absolute_error(RFR.predict(x_test), y_test)
RandomForest_MSE = metrics.mean_squared_error(RFR.predict(x_test), y_test, squared=False)

SVM_MAE = metrics.mean_absolute_error(SVR.predict(x_test), y_test)
SVM_MSE = metrics.mean_squared_error(SVR.predict(x_test), y_test, squared=False)

Report = {'Regression Algorithm': ['Linear Regression','Random Forest', 'SVM'],
          'MAE': [LinearRegression_MAE,RandomForest_MAE, SVM_MAE],
          'RMSE': [LinearRegression_MSE,RandomForest_MSE, SVM_MSE],}
Report = pd.DataFrame(Report)
Report


#visualization of accuracies
fig = make_subplots(rows=1, cols=1)

fig.add_trace(
    go.Bar(x=Report['Regression Algorithm'], y=Report['MAE'], name='MAE'),
    row=1, col=1
)

fig.add_trace(
    go.Bar(x=Report['Regression Algorithm'], y=Report['RMSE'], name='RMSE'), row=1,col=1
)
fig.update_layout(title_text="Error of Regression Algorithms")
fig.update_yaxes(title_text="Error", row=1, col=1)
fig.update_xaxes(title_text="Regression Algorithm", row=1, col=1)
fig.show()

	Date	WindSpeed9am	WindSpeed3pm
0		14.761316	19.335958
1	0	15.250000	19.649123
2	1	15.088106	19.427313
3	2	15.938326	19.748899
4	3	15.317181	19.462555
5	4	15.898678	19.061674
6	5	15.555066	18.735683
7	6	15.123348	18.960352
8	7	15.215859	19.189427
9	8	14.356828	19.030837
10	9	14.418502	19.493392

	Date	Humidity9am	Humidity3pm
0		68.253990	54.931767
1	0	68.973684	55.039474
2	1	67.233480	53.837004
3	2	68.541850	54.237885
4	3	67.590308	54.268722
5	4	68.356828	56.392070
6	5	66.969163	52.599119
7	6	68.792952	55.246696
8	7	68.418502	54.594714
9	8	67.753304	54.674009
10	9	69.762115	55.066079

	Date	Pressure9am	Pressure3pm
0		1018.534500	1016.175128
1	0	1018.384649	1016.080263
2	1	1017.613656	1015.242731
3	2	1018.659031	1016.343172
4	3	1018.268282	1015.970925
5	4	1018.127313	1015.986784
6	5	1018.383260	1016.066079
7	6	1018.394714	1015.975132
8	7	1017.971806	1015.606608
9	8	1018.233921	1015.918767
10	9	1018.425110	1016.081396

	Temp9am	Temp3pm	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustSpeed	WindSpeed9am	WindSpeed3pm	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm
Temp9am	1.000000	0.828457	0.939307	0.858639	-0.054297	0.606122	0.113891	0.159570	-0.334084	0.280009	-0.145944	0.131855	-0.417437	-0.401353	0.093204	0.068683
Temp3pm	0.828457	1.000000	0.753147	0.960805	-0.142748	0.474599	0.345528	0.036216	-0.351836	0.136713	-0.155192	-0.212696	-0.342862	-0.387945	-0.086544	-0.144965
MinTemp	0.939307	0.753147	1.000000	0.771005	0.013995	0.569316	-0.063475	0.127792	-0.298040	0.225905	0.053946	0.273269	-0.391654	-0.353334	0.241248	0.164028
MaxTemp	0.858639	0.960805	0.771005	1.000000	-0.139306	0.510334	0.327422	0.073184	-0.349382	0.155542	-0.185382	-0.150741	-0.385853	-0.420375	-0.084695	-0.099217
Rainfall	-0.054297	-0.142748	0.013995	-0.139306	1.000000	-0.110343	-0.308962	0.149811	0.153696	0.039415	0.332636	0.305635	-0.037561	0.001780	0.245231	0.200999
Evaporation	0.606122	0.474599	0.569316	0.510334	-0.110343	1.000000	0.175932	0.245426	-0.077532	0.292469	-0.395651	-0.102295	-0.343581	-0.298303	-0.037051	-0.038422
Sunshine	0.113891	0.345528	-0.063475	0.327422	-0.308962	0.175932	1.000000	-0.029032	-0.058410	0.177813	-0.490631	-0.586476	-0.047789	-0.094507	-0.665014	-0.677861
WindGustSpeed	0.159570	0.036216	0.127792	0.073184	0.149811	0.245426	-0.029032	1.000000	0.256460	0.509825	-0.225613	-0.095037	-0.408876	-0.351850	0.024275	0.044016
WindSpeed9am	-0.334084	-0.351836	-0.298040	-0.349382	0.153696	-0.077532	-0.058410	0.256460	1.000000	0.197669	-0.189763	-0.148849	0.003737	0.091878	-0.062441	-0.018030
WindSpeed3pm	0.280009	0.136713	0.225905	0.155542	0.039415	0.292469	0.177813	0.509825	0.197669	1.000000	-0.288910	-0.135039	-0.333810	-0.277749	-0.050932	-0.095435
Humidity9am	-0.145944	-0.155192	0.053946	-0.185382	0.332636	-0.395651	-0.490631	-0.225613	-0.189763	-0.288910	1.000000	0.657933	0.262098	0.249202	0.431550	0.331062
Humidity3pm	0.131855	-0.212696	0.273269	-0.150741	0.305635	-0.102295	-0.586476	-0.095037	-0.148849	-0.135039	0.657933	1.000000	0.151608	0.179611	0.463529	0.472248
Pressure9am	-0.417437	-0.342862	-0.391654	-0.385853	-0.037561	-0.343581	-0.047789	-0.408876	0.003737	-0.333810	0.262098	0.151608	1.000000	0.963307	-0.015889	-0.039509
Pressure3pm	-0.401353	-0.387945	-0.353334	-0.420375	0.001780	-0.298303	-0.094507	-0.351850	0.091878	-0.277749	0.249202	0.179611	0.963307	1.000000	0.013787	-0.003536
Cloud9am	0.093204	-0.086544	0.241248	-0.084695	0.245231	-0.037051	-0.665014	0.024275	-0.062441	-0.050932	0.431550	0.463529	-0.015889	0.013787	1.000000	0.604359
Cloud3pm	0.068683	-0.144965	0.164028	-0.099217	0.200999	-0.038422	-0.677861	0.044016	-0.018030	-0.095435	0.331062	0.472248	-0.039509	-0.003536	0.604359	1.000000

	Temp9am	Temp3pm	MinTemp	MaxTemp	Rainfall	RainToday	Evaporation	Sunshine	WindGustDir	WindGustSpeed	WindDir9am	WindDir3pm	WindSpeed9am	WindSpeed3pm	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm
count	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000	3272.000000
mean	17.821461	21.543656	14.877102	23.005564	3.342158	0.259474	5.175787	7.168970	9.533619	41.476307	10.185819	5.819682	15.077041	19.294405	68.243962	54.698563	1018.334424	1016.003085	4.318557	4.176093
std	4.893568	4.296396	4.554014	4.483067	9.916230	0.438413	2.757262	3.815383	4.594878	10.805299	4.424958	4.993595	7.042748	7.452191	15.083821	16.276753	7.019017	7.018842	2.526537	2.410905
min	6.400000	10.200000	4.300000	11.700000	0.000000	0.000000	0.000000	0.000000	0.000000	17.000000	0.000000	0.000000	0.000000	0.000000	19.000000	10.000000	986.700000	989.800000	0.000000	0.000000
25%	13.800000	18.400000	11.000000	19.600000	0.000000	0.000000	3.200000	4.275000	5.000000	35.000000	8.000000	1.000000	11.000000	15.000000	58.000000	44.000000	1013.700000	1011.300000	2.000000	2.000000
50%	18.200000	21.300000	14.900000	22.850000	0.000000	0.000000	4.800000	8.300000	13.000000	41.000000	13.000000	4.000000	15.000000	19.000000	69.000000	56.000000	1018.600000	1016.300000	5.000000	4.000000
75%	21.700000	24.500000	18.800000	26.000000	1.400000	1.000000	7.000000	10.200000	13.000000	44.000000	13.000000	10.000000	20.000000	24.000000	80.000000	64.000000	1023.100000	1020.800000	7.000000	7.000000
max	36.500000	44.700000	27.600000	45.800000	119.400000	1.000000	18.400000	13.600000	15.000000	96.000000	15.000000	15.000000	54.000000	57.000000	100.000000	99.000000	1039.000000	1036.700000	9.000000	8.000000

	Temp9am	Temp3pm	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustSpeed	WindSpeed9am	WindSpeed3pm	...	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Date	RainToday	WindGustDir	WindDir9am	WindDir3pm
0	0.588319	-0.149836	1.015281	-0.135099	1.236328	0.371517	-1.879252	-0.044088	0.273083	0.094697	...	1.800477	-0.104649	0.199054	1.457333	1.586330	36	1	13	8	11
1	0.935767	0.758040	1.015281	0.578807	0.268070	-0.644138	-1.171482	-0.044088	-0.863011	-0.844767	...	1.124563	-0.061902	0.056559	1.061474	1.171485	1220	1	13	13	0
2	1.160586	0.339020	1.476483	0.333402	0.328587	-1.006872	-1.853038	-0.044088	0.273083	-2.321069	...	1.923370	-0.232892	-0.057438	1.061474	1.586330	2391	1	13	2	2
3	0.731386	-0.149836	1.169015	-0.045860	1.559081	-1.079419	-1.879252	-0.044088	0.983141	0.094697	...	2.169157	-0.589122	-0.598920	1.457333	1.586330	2659	1	13	5	0
4	0.956205	0.920992	1.059205	0.601117	7.469488	-0.136311	-1.879252	-0.044088	-0.578988	-1.784232	...	1.186010	-1.429824	-1.596388	1.457333	1.586330	2767	1	13	5	13

	Classification Algorithm	Accuracy Score	F1-score
0	Logistic Regression	0.826353	0.613592
1	KNN	0.809773	0.835327
2	Decision Tree	0.749564	0.524046
3	SVM	0.823735	0.600791
4	Random Forest	0.834206	0.609053
5	Ensembled Classifier	0.828098	0.597137

	Regression Algorithm	MAE	RMSE
0	Linear Regression	0.482365	0.870991
1	Random Forest	0.373415	0.813171
2	SVM	0.333742	0.879352

	Regression Algorithm	MAE	RMSE
0	Linear Regression	0.470812	0.598336
1	Random Forest	0.439175	0.558385
2	SVM	0.424299	0.546106